Owners, creators, and distributors of media suitable for broadcast or streaming over the Internet need to understand the preferences and desires of their viewers (consumers). This is essential in decision making as to what media will be developed, distributed, streamed, or broadcast and what means will be used to distribute. A reliable and robust demand forecasting tool is desired.
Traditionally, audience demand is grossly estimated via polling and surveys. The key problems with this technique are (1) The technique is not able to segment the audience with enough granularity to target a sub-audience, (2) The technique can be slow (not real-time), relatively expensive, and most importantly, (3) Modern media content consumers do not communicate via polls and surveys—they use social media and other highly dynamic and temporally sensitive sources. Furthermore, even the limited audience segmentation that is possible with conventional methods is further constrained in the requirement that the parameters used for segmentation must be known beforehand. A desirable outcome would enable segmentation based on any emergent set of parameters that identify an audience segment for the purposes of profiling.
The present invention leverages the ubiquity of online products and services (e.g. forums, blogs, wikis, and social media) as well as offline sources (e.g. customer loyalty databases, consumer product registrations) to accurately and efficiently predict viewer sentiment of a specific audience pertaining to any type of broadcast or streaming media.
There are many ways that multiple data sources can be aggregated to make generalizations (e.g. support vector machine, Gaussian-process leaning, fuzzy-logic inference system, neural networks, Bayesian networks, evolutionary or genetic computation). The uniqueness of the present invention within the broadcast and streaming media domain is that the “audience” can be characterized in infinitely many (and often unpredictable) ways along multiple relevant attributes (e.g. age, geography, hobbies, social status) and these different audiences can be linked to attributes of the media in infinitely many ways. Most importantly, there are commonalities across audiences and across topics that can be useful in computing a generalization.
Multi-task learning (MTL) is one such machine learning technique that facilitates learning multiple simultaneous and related tasks using a shared symbolic representation. While the tasks' target outputs may be related, the data sources that drive the learning can be related or may be seemingly unrelated attribute-wise.
The present invention uses multi-label, multi-class classification adapted to the unique demands and characteristics of the broadcast and streaming media industry to “profile” an audience, thus predicting that specific audience's sentiment about that content to inform the content holder regarding demand forecasting services.